Decision Support · Side-by-side
Compare pricing, strengths, and use cases so it is easier to pick the right fit.
Change tools
Neither Devin AI nor Magic is ready for everyday users—both are enterprise-only, lack mobile apps, and require significant technical setup. Devin AI is the more practical choice for professional software teams needing an autonomous coding agent that integrates with existing tools like GitHub and Jira. Magic, while technically impressive with its 100M-token context window, remains in early access and is better suited for researchers pushing AGI boundaries than for daily development tasks.
Devin AI
Magic
Scores at a glance
Choose Devin AI if
Choose Magic if
Key differences
Facts side by side
| Devin AI | Magic | |
|---|---|---|
| Free plan | ||
| Mobile app | ||
| API access |
Common questions
No, neither tool has a mobile app. Both require a desktop environment with access to your codebase.
Neither is suitable—both are enterprise-only. Solo developers should look at tools like GitHub Copilot or Cursor instead.
Yes, Magic's 100M-token context window lets it process entire large codebases in one pass, while Devin AI works on smaller chunks. But Magic is harder to access and less documented.
Neither publishes pricing. Devin AI is sold through enterprise sales; Magic is in early access with no public pricing. Expect both to be expensive and out of reach for individuals.
Devin AI is easier because it integrates with common tools and has clearer documentation. Magic requires joining a waitlist, installing plugins, and configuring a local context map—much more complex.
Devin AI wins for practical enterprise coding; Magic is a futuristic research tool—neither is for everyday users.
If you're a regular person or small team, skip both for now—they're built for big companies with big budgets. Devin AI is the more practical choice if you're in an enterprise setting and want an autonomous coding assistant that fits your existing workflow. Magic is only worth considering if you're a researcher or have a massive codebase and are willing to deal with early-access hurdles.
